API 579 Level 3 Assessment of Dents Using High-Resolution ILI Data

Abstract

In-line Inspection (ILI) surveys are periodically performed to determine the condition of the pipeline. Typical ILI surveys involve Magnetic Flux Leakage primarily to determine metal loss and simple single channel Calliper surveys to determine any signs of geometry imperfections. Additional surveys such as high-resolution multi-channel Calliper deformation tools are occasionally used to accurately record imperfections to enable a more accurate assessment of the integrity of the pipeline containing the imperfection. Such tools have had limited employment, and therefore little experience exists of using the data obtainable for the detailed assessment of defects. This paper presents a study of such a case. As part of an In-line Inspection (ILI) of an offshore pipeline, a high-resolution deformation survey recorded numerous dent anomalies which had potentially resulted from a single dragged anchor incident before the pipeline was trenched. This data set was correlated to Magnetic Flux Leakage inspection data to confirm external mechanical damage. Pipeline sections having anomalies that were either found close to girth welds, or had associated corrosion defects were automatically selected for repair. The remaining anomalies were assessed in order to determine their acceptability for the maximum allowable operating pressure using the approaches detailed in API-579. Due to the sharp nature of some of the dents, elastic-plastic finite element analyses (FEA) were performed using denting profiles generated from the calliper data of the ILI run. API-579 level 3 assessments were then carried out using the FEA results. This paper details the high-resolution deformation tool findings and the approach used in order to assess the fitness-for-purpose of the pipe with the recorded anomalies.

Theory and Application of Magnetic Flux Leakage Pipeline Detection

“…The defect appearances are derived from the magnetic flux leakage signals, including the distinction between different kinds of defects and to obtain the characteristic information of qualitative analysis and the defect shape parameters to make quantitative analysis, can be attributed to a typical inverse electromagnetic field problem. The inverse electromagnetic field problem can be divided into two categories: the optimal design problem and the parameter identification problem [ 86 , 87 , 88 , 89 , 90 ]. The former is also known as a comprehensive problem.…”